Hits identification
Initialization
We start the analysis by initializing the packages required for all the analysis performed in this section. We also define the root directory, within which all the input/output operations for this project will be performed. At the end of this document, detailed software version information is provided for easier reproducibility of the analysis.
library(dplyr)
library(reshape2)
library(plotly)
library(ggplot2)
library(RColorBrewer)
library(ggrepel)
library(ggpubr)
library(gplots)
library(UpSetR)
library(venn)
library(VennDiagram)
library(DT)
path = "/Users/ashwin/Documents/Projects/YeastScreen/Nonessential_MATa_screen/"
genesets = readRDS(paste0(path, "data/workspaces/YeastGeneSets.RDS"))Hits identification
Here we show an interactive way to to access the normalized screening data. The red, grey and green horizontal lines highlight the 95th quantile, median and 5th quantile of roGFP2 values repectively in that condition.
normDat = readRDS(paste0(path, "data/workspaces/YeastMutantRedox_NormalizedData_RobustZ.RDS"))
hits = normDat$redox_table %>% group_by(Organelle, Nutrient) %>% arrange(-Median_roGFP2_ratio) %>%
mutate(Rank1 = dense_rank(-Median_roGFP2_ratio)) %>% mutate(Rank2 = dense_rank(Median_roGFP2_ratio)) %>%
mutate(Median_roGFP2_ratio = round(Median_roGFP2_ratio, 2)) %>% mutate(MedVal = median(Median_roGFP2_ratio)) %>%
mutate(upperquant = quantile(Median_roGFP2_ratio, 0.95)) %>% mutate(lowerquant = quantile(Median_roGFP2_ratio,
0.05)) %>% ungroup()
p_print = ggplot(hits, aes(x = Rank1, y = Median_roGFP2_ratio)) + theme_bw(base_size = 7) +
labs(x = "Sorted Ranks of mutants (high to low roGFP2 ratios)", y = "Normalized by plate median (roGFP2 ratios)") +
geom_point(size = 0.3) +
facet_wrap(Organelle ~ Nutrient, scales = "free", ncol = 6) +
geom_hline(aes(yintercept = MedVal), col = "grey20") + geom_hline(aes(yintercept = upperquant),
col = "firebrick") + geom_hline(aes(yintercept = lowerquant), col = "forestgreen") +
theme(axis.text.x = element_blank(), axis.ticks = element_blank(), axis.text = element_text(colour = "black"),
strip.background = element_blank(), panel.border = element_rect(size = 0.3),
panel.grid = element_blank()) +
geom_text(data = hits[hits$Rank1 <= 10, ], aes(x = 2500, label = Genes), size = 2) +
geom_text(data = hits[hits$Rank2 <= 10, ], aes(x = 2500, label = Genes), size = 2)
pdf(paste0(path, "analysis/tophits/top_and_low_10_hits.pdf"), width = 7.5, height = 2.5)
print(p_print)
invisible(dev.off())
p_html = ggplot(hits) + theme_minimal(base_size = 9) + labs(x = "Sorted Ranks of mutants (high to low roGFP2 ratios)",
y = "Normalized by plate median (roGFP2 ratios)") + geom_point(aes(text = Genes,
x = Rank1, y = Median_roGFP2_ratio)) + facet_wrap(Organelle ~ Nutrient, scales = "free",
ncol = 3) + geom_hline(aes(yintercept = MedVal), col = "grey20") + geom_hline(aes(yintercept = upperquant),
col = "firebrick") + geom_hline(aes(yintercept = lowerquant), col = "forestgreen") +
theme(axis.text.x = element_text(angle = 90, hjust = 0.5, vjust = 0.5))
ggplotly(p_html) %>% toWebGL()Hits overlaps
Next we look at the overlaps between top hits i.e topmost 5% and lowest 5% of roGFP2 ratios across different nutrient conditions and organelles. This corresponds to the hits (dots) above the red and below the green horizontal lines in the above figure.
#Cytoplasm
cyto = normDat$redox_table[normDat$redox_table$Organelle == "Cytoplasm",]
cyto = split(cyto, cyto$Nutrient)
#Mitochondria
mito = normDat$redox_table[normDat$redox_table$Organelle == "Mitochondria",]
mito = split(mito, mito$Nutrient)
cytoOX = lapply(cyto, function(x){
genes = x$Genes
vals = x$Median_roGFP2_ratio
qt.top = quantile(vals, 0.95)
selgenes = unique(genes[vals > qt.top])
})
mitoOX = lapply(mito, function(x){
genes = x$Genes
vals = x$Median_roGFP2_ratio
qt.top = quantile(vals, 0.95)
selgenes = unique(genes[vals > qt.top])
})
cytoRX = lapply(cyto, function(x){
genes = x$Genes
vals = x$Median_roGFP2_ratio
qt.low = quantile(vals, 0.05)
selgenes = unique(genes[vals < qt.low])
})
mitoRX = lapply(mito, function(x){
genes = x$Genes
vals = x$Median_roGFP2_ratio
qt.low = quantile(vals, 0.05)
selgenes = unique(genes[vals < qt.low])
})
topHits = list(
Cytoplasm_Glucose_high_roGFP2 = cytoOX$Glucose,
Cytoplasm_Galactose_high_roGFP2 = cytoOX$Galactose,
Cytoplasm_Glycerol_high_roGFP2 = cytoOX$Glycerol,
Mitochondria_Glucose_high_roGFP2 = mitoOX$Glucose,
Mitochondria_Galactose_high_roGFP2 = mitoOX$Galactose,
Mitochondria_Glycerol_high_roGFP2 = mitoOX$Glycerol,
Cytoplasm_Glucose_low_roGFP2 = cytoRX$Glucose,
Cytoplasm_Galactose_low_roGFP2 = cytoRX$Galactose,
Cytoplasm_Glycerol_low_roGFP2 = cytoRX$Glycerol,
Mitochondria_Glucose_low_roGFP2 = mitoRX$Glucose,
Mitochondria_Galactose_low_roGFP2 = mitoRX$Galactose,
Mitochondria_Glycerol_low_roGFP2 = mitoRX$Glycerol)
topPaths = function(dat)
{
a = Reduce(intersect, dat)
# KEGG
gs1 = sapply(genesets$KEGG, function(x){sum(a %in% x)/length(a)*100})
gna = sapply(genesets$KEGG, function(x){paste(a[a %in% x], collapse = ";")})
gs1 = data.frame(Geneset = names(gs1), Type = "KEGG", PercentOverlap = round(as.numeric(gs1),2), Genes = gna)
rownames(gs1) = 1:nrow(gs1)
gs = gs1[gs1$PercentOverlap > 0,]
rm(gs1, gna)
# GOBP
gs1 = sapply(genesets$GOBP, function(x){sum(a %in% x)/length(a)*100})
gna = sapply(genesets$GOBP, function(x){paste(a[a %in% x], collapse = ";")})
gs1 = data.frame(Geneset = names(gs1), Type = "GOBP", PercentOverlap = round(as.numeric(gs1),2), Genes = gna)
rownames(gs1) = 1:nrow(gs1)
gs = rbind(gs, gs1[gs1$PercentOverlap > 0,])
rm(gs1, gna)
# GOMF
gs1 = sapply(genesets$GOMF, function(x){sum(a %in% x)/length(a)*100})
gna = sapply(genesets$GOMF, function(x){paste(a[a %in% x], collapse = ";")})
gs1 = data.frame(Geneset = names(gs1), Type = "GOMF", PercentOverlap = round(as.numeric(gs1),2), Genes = gna)
rownames(gs1) = 1:nrow(gs1)
gs = rbind(gs, gs1[gs1$PercentOverlap > 0,])
rm(gs1, gna)
# Phenotype
gs1 = sapply(genesets$Phenotype, function(x){sum(a %in% x)/length(a)*100})
gna = sapply(genesets$Phenotype, function(x){paste(a[a %in% x], collapse = ";")})
gs1 = data.frame(Geneset = names(gs1), Type = "Phenotypes", PercentOverlap = round(as.numeric(gs1),2), Genes = gna)
rownames(gs1) = 1:nrow(gs1)
gs = rbind(gs, gs1[gs1$PercentOverlap > 0,])
rm(gs1, gna)
gs = gs[order(gs$PercentOverlap, decreasing = T),]
return(gs)
}
topHits_allcarbon = list(common_hits = list(
Cytoplasm_high_roGFP2_commom = Reduce(intersect, cytoOX),
Cytoplasm_low_roGFP2_commom = Reduce(intersect, cytoRX),
Mitochondria_high_roGFP2_commom = Reduce(intersect, mitoOX),
Mitochondria_low_roGFP2_commom = Reduce(intersect, mitoRX)
),
common_pathways = list(
Cytoplasm_high_roGFP2_commom_path = topPaths(cytoOX),
Cytoplasm_low_roGFP2_commom_path = topPaths(cytoRX),
Mitochondria_high_roGFP2_commom_path = topPaths(mitoOX),
Mitochondria_low_roGFP2_commom_path = topPaths(mitoRX)
)
)
rm(cyto, mito, cytoRX, cytoOX, mitoRX, mitoOX, topPaths)We show venn diagrams and correponding tables (only for the common hits across all nutrient condition) for the overlaps between top hits i.e topmost 5% and lowest 5% of roGFP2 ratios across cytoplasm and mitochondria
Common hits TOPMOST 5% roGFP2 ratios: Cytoplasm - all nutrient conditions
par(mar = c(1, 1, 1, 1), xpd = T)
venn::venn(topHits[1:3], box = F, zcolor = brewer.pal(6, "Set1"), plotsize = 30)
The common hits across all nutrient conditions are -
[1] "ENV10" "PER33" "SPT8" "YLR046C" "YLR062C" "RGM1" "PDE2"
[8] "ERV15" "PCS60" "RRD2" "LRP1" "LST4" "SSH4" "YGR035C"
[15] "RPS12" "YEA4" "EMP24" "HAL1" "IRC7" "HAL9" "PAP2"
[22] "YBT1" "MRPL17" "YNR065C" "YNR068C" "PBI2" "LRO1" "SNF12"
[29] "YOR333C" "YPT53" "OXA1" "MTM1" "MRP1" "MRP17" "MSS116"
[36] "THO2" The overlap of the above genes with genesets from KEGG, GO BP/MF and Phenotypes -
datatable(topHits_allcarbon$common_pathways$Cytoplasm_high_roGFP2_commom_path, rownames = FALSE,
filter = "top", class = "compact", extensions = c("Buttons"), options = list(autoWidth = TRUE,
dom = "Blfrtip", buttons = c("csv", "excel"))) %>% formatStyle("Geneset",
`white-space` = "nowrap")The roGFP2 ratios (redox scores) for the common hits (topmost 5%) -
cyto = normDat$redox_table[normDat$redox_table$Organelle == "Cytoplasm", ]
tmpdat = cyto[cyto$Genes %in% topHits_allcarbon$common_hits$Cytoplasm_high_roGFP2_commom,
]
tmpdat[, 5:9] = round(tmpdat[, 5:9], 3)
datatable(tmpdat, rownames = FALSE, filter = "top", class = "compact", extensions = c("Buttons"),
options = list(autoWidth = TRUE, dom = "Blfrtip", buttons = c("csv", "excel")))Common hits LOWEST 5% roGFP2 ratios: Cytoplasm - all nutrient conditions
par(mar = c(1, 1, 1, 1), xpd = T)
venn::venn(topHits[7:9], box = F, zcolor = brewer.pal(6, "Set1"), plotsize = 30)
The common hits across all nutrient conditions are -
[1] "VPS8" "YML089C" "SRC1" "SIP3" "SNZ2" "PEX15"
[7] "ISC1" "SLI1" "RFM1" "YOR214C" "MRS3" "SAP155"
[13] "LYS9" "SIR1" "YNR066C" "YOR008C-A" "YIL060W" "FUR4"
[19] "YLR358C" "ATP22" "CBP1" "CBS2" "CYT1" "MTF1"
[25] "PET309" The overlap of the above genes with genesets from KEGG, GO BP/MF and Phenotypes -
datatable(topHits_allcarbon$common_pathways$Cytoplasm_low_roGFP2_commom_path, rownames = FALSE,
filter = "top", class = "compact", extensions = c("Buttons"), options = list(autoWidth = TRUE,
dom = "Blfrtip", buttons = c("csv", "excel"))) %>% formatStyle("Geneset",
`white-space` = "nowrap")The roGFP2 ratios (redox scores) for the common hits (lowest 5%) -
cyto = normDat$redox_table[normDat$redox_table$Organelle == "Cytoplasm", ]
tmpdat = cyto[cyto$Genes %in% topHits_allcarbon$common_hits$Cytoplasm_low_roGFP2_commom,
]
tmpdat[, 5:9] = round(tmpdat[, 5:9], 3)
datatable(tmpdat, rownames = FALSE, filter = "top", class = "compact", extensions = c("Buttons"),
options = list(autoWidth = TRUE, dom = "Blfrtip", buttons = c("csv", "excel")))Common hits TOPMOST 5% roGFP2 ratios: Mitochondria - all nutrient conditions
par(mar = c(1, 1, 1, 1), xpd = T)
venn::venn(topHits[4:6], box = F, zcolor = brewer.pal(6, "Set1"), plotsize = 30)
The common hits across all nutrient conditions are -
[1] "TYE7" "YPR1" "CUE2" "RPS25A" "HOM2" "KGD2" "EMP24" "SAC1"
[9] "DPP1" "COS10" "MPD2" "SNO1" "UBA3" "PTR2" "SRL3" "TGL4"
[17] "TVP38" "SFC1" "PUB1" "MET18" "RIM15" "THO2" The overlap of the above genes with genesets from KEGG, GO BP/MF and Phenotypes -
datatable(topHits_allcarbon$common_pathways$Mitochondria_high_roGFP2_commom_path,
rownames = FALSE, filter = "top", class = "compact", extensions = c("Buttons"),
options = list(autoWidth = TRUE, dom = "Blfrtip", buttons = c("csv", "excel"))) %>%
formatStyle("Geneset", `white-space` = "nowrap")The roGFP2 ratios (redox scores) for the common hits (topmost 5%) -
cyto = normDat$redox_table[normDat$redox_table$Organelle == "Mitochondria", ]
tmpdat = cyto[cyto$Genes %in% topHits_allcarbon$common_hits$Mitochondria_high_roGFP2_commom,
]
tmpdat[, 5:9] = round(tmpdat[, 5:9], 3)
datatable(tmpdat, rownames = FALSE, filter = "top", class = "compact", extensions = c("Buttons"),
options = list(autoWidth = TRUE, dom = "Blfrtip", buttons = c("csv", "excel")))Common hits LOWEST 5% roGFP2 ratios: Mitochondria - all nutrient conditions
par(mar = c(1, 1, 1, 1), xpd = T)
venn::venn(topHits[10:12], box = F, zcolor = brewer.pal(6, "Set1"), plotsize = 30)
The common hits across all nutrient conditions are -
[1] "YML089C" "NGL2" "SCS7" "TMA23" "YMR295C" "ATG29" "BIM1"
[8] "GND1" "KEL1" "SCM4" "GSP2" "SER1" "THI72" "SET2"
[15] "TPK1" "PIM1" "PSY4" "YNL228W" "MNT3" "COX12" "RIC1"
[22] "YCL075W" "YGR018C" "YNL120C" "41548" "ATP7" "CYT2" "DID4"
[29] "MMM1" "PEP3" The overlap of the above genes with genesets from KEGG, GO BP/MF and Phenotypes -
datatable(topHits_allcarbon$common_pathways$Mitochondria_low_roGFP2_commom_path,
rownames = FALSE, filter = "top", class = "compact", extensions = c("Buttons"),
options = list(autoWidth = TRUE, dom = "Blfrtip", buttons = c("csv", "excel"))) %>%
formatStyle("Geneset", `white-space` = "nowrap")The roGFP2 ratios (redox scores) for the common hits (lowest 5%) -
cyto = normDat$redox_table[normDat$redox_table$Organelle == "Mitochondria", ]
tmpdat = cyto[cyto$Genes %in% topHits_allcarbon$common_hits$Mitochondria_low_roGFP2_commom,
]
tmpdat[, 5:9] = round(tmpdat[, 5:9], 3)
datatable(tmpdat, rownames = FALSE, filter = "top", class = "compact", extensions = c("Buttons"),
options = list(autoWidth = TRUE, dom = "Blfrtip", buttons = c("csv", "excel")))redoxVenn = function(index, col.groups, col.circles, col.main)
{
vennP = venn.diagram(
x = topHits[index],
category.names = sapply(strsplit(names(topHits), "_"), function(x)x[2])[index],
main = sapply(strsplit(names(topHits), "_"), function(x)x[1])[index[1]],
filename = NULL,
lwd = 0.7,
col = col.circles,
main.col = col.main,
cex = 0.5,
fontfamily = "sans",
main.cex = 0.5,
cat.cex = 0.5,
cat.default.pos = "outer",
cat.pos = c(-30, 30, 180),
cat.dist = c(0.05, 0.05, 0.05),
cat.fontfamily = "sans",
cat.col = col.groups)
return(vennP)
}
# Cyto - Glu, Gal, Gly - HIGH roGFP2
venn1 = redoxVenn(index = 1:3, col.groups = rep("grey70",3), col.circles = rep("#252525",3), col.main ="#A6611A")
# Mito - Glu, Gal, Gly - HIGH roGFP2
venn2 = redoxVenn(index = 4:6, col.groups = rep("grey70",3), col.circles = rep("#252525",3), col.main ="#7B3294")
# Cyto - Glu, Gal, Gly - LOW roGFP2
venn3 = redoxVenn(index = 7:9, col.groups = rep("grey70",3), col.circles = rep("#bdbdbd",3), col.main ="#A6611A")
# Mito - Glu, Gal, Gly - LOW roGFP2
venn4 = redoxVenn(index = 10:12, col.groups = rep("grey70",3), col.circles = rep("#bdbdbd",3), col.main ="#7B3294")
# PLOT
pdf(paste0(path, 'analysis/tophits/compartment_specific_venn_top_low_5per.pdf'), width = 7, height = 3)
# layout
gl <- grid.layout(nrow = 2, ncol = 4)
# setup viewports
vp.1 <- viewport(layout.pos.col = 1, layout.pos.row = 1)
vp.2 <- viewport(layout.pos.col = 2, layout.pos.row = 1)
vp.3 <- viewport(layout.pos.col = 3, layout.pos.row = 1)
vp.4 <- viewport(layout.pos.col = 4, layout.pos.row = 1)
vp.5 <- viewport(layout.pos.col = 1, layout.pos.row = 2)
vp.6 <- viewport(layout.pos.col = 2, layout.pos.row = 2)
vp.7 <- viewport(layout.pos.col = 3, layout.pos.row = 2)
vp.8 <- viewport(layout.pos.col = 4, layout.pos.row = 2)
# init layout
pushViewport(viewport(layout = gl))
pushViewport(vp.1)
grid.text(label = topHits_allcarbon$common_pathways$Cytoplasm_high_roGFP2_commom_path$Geneset[1:10],
x = 0.99, y = seq(0.9, 0.11, -0.08), just = "right", gp=gpar(fontsize=6, col="grey"))
popViewport()
pushViewport(vp.2)
grid.draw(venn1)
popViewport()
pushViewport(vp.3)
grid.draw(venn2)
popViewport()
pushViewport(vp.4)
grid.text(label = topHits_allcarbon$common_pathways$Mitochondria_high_roGFP2_commom_path$Geneset[1:10],
x = 0.01, y = seq(0.9, 0.11, -0.08), just = "left", gp=gpar(fontsize=6, col="grey"))
popViewport()
pushViewport(vp.5)
grid.text(label = topHits_allcarbon$common_pathways$Cytoplasm_low_roGFP2_commom_path$Geneset[1:10],
x = 0.99, y = seq(0.9, 0.11, -0.08), just = "right", gp=gpar(fontsize=6, col="grey"))
popViewport()
pushViewport(vp.6)
grid.draw(venn3)
popViewport()
pushViewport(vp.7)
grid.draw(venn4)
popViewport()
pushViewport(vp.8)
grid.text(label = topHits_allcarbon$common_pathways$Mitochondria_low_roGFP2_commom_path$Geneset[1:10],
x = 0.01, y = seq(0.9, 0.11, -0.08), just = "left", gp=gpar(fontsize=6, col="grey"))
popViewport()
popViewport(1)
rm(gl, vp.1, vp.2, vp.3, vp.4, vp.5, vp.6, vp.7, vp.8, venn1, venn2, venn3, venn4)
invisible(dev.off())[1] TRUEPositive control test
Next we tried to validate the screening results by looking at the roGFP2 ratios of mutants know to affect oxidation taken from Aung-Htut et.al
# rdx1 = genesets$GOBP$`response to oxidative stress` rdx2 =
# genesets$GOMF$`oxidoreductase activity` rdx3 = genesets$KEGG$`Oxidative
# phosphorylation` rdx4 = genesets$KEGG$`Glutathione metabolism` rdx1 =
# unique(c(rdx1, rdx2, rdx3, rdx4)) rdx2 = unique(c(rdx3, rdx4))
# #------------------- rdx3 = c( grep('GPX', rdx1, value = T), grep('GRX', rdx1,
# value = T), grep('GSH', rdx1, value = T), grep('SDH', rdx1, value = T),
# grep('ATP', rdx1, value = T), grep('COX', rdx1, value = T), grep('COR', rdx1,
# value = T), grep('SOD', rdx1, value = T), grep('TRX', rdx1, value = T),
# grep('TRR', rdx1, value = T) ) rdx3
# Manisha selected redox genes
rdx = c("MSN2", "MSN4", "YAP1", "SKN7", "GND1", "GND2", "TAL1", "SOL3", "SOL4", "RPE1",
"TKL1", "ALD6", "IDP1", "POS5", "IDP2", "IDP3", "ALD4", "ALD5", "YEF1", "UTR1",
"ZWF1", "MAE1", "GLR1", "GPX1", "GPX2", "GPX3", "GRX1", "GRX2", "GRX3", "GRX4",
"GRX5", "GRX6", "GRX7", "GRX8", "GSH1", "GSH2", "TRX1", "TRX2", "TRX3", "TRR1",
"TRR2", "PRX1", "TSA1", "TSA2", "AHP1", "DOT5", "CTT1", "CTA1", "SOD1", "SOD2",
"CCP1")
rdx [1] "MSN2" "MSN4" "YAP1" "SKN7" "GND1" "GND2" "TAL1" "SOL3" "SOL4" "RPE1"
[11] "TKL1" "ALD6" "IDP1" "POS5" "IDP2" "IDP3" "ALD4" "ALD5" "YEF1" "UTR1"
[21] "ZWF1" "MAE1" "GLR1" "GPX1" "GPX2" "GPX3" "GRX1" "GRX2" "GRX3" "GRX4"
[31] "GRX5" "GRX6" "GRX7" "GRX8" "GSH1" "GSH2" "TRX1" "TRX2" "TRX3" "TRR1"
[41] "TRR2" "PRX1" "TSA1" "TSA2" "AHP1" "DOT5" "CTT1" "CTA1" "SOD1" "SOD2"
[51] "CCP1"We now check if these positive controls are observed in topmost or lowest 25% of the normalized roGFP2 ratios. NOTE: that the 25% cutoff is more liberal than the 5% cutoff we have taken for all our analysis above
#Cytoplasm
cyto = normDat$redox_table[normDat$redox_table$Organelle == "Cytoplasm",]
cyto = split(cyto, cyto$Nutrient)
#Mitochondria
mito = normDat$redox_table[normDat$redox_table$Organelle == "Mitochondria",]
mito = split(mito, mito$Nutrient)
cytoOX = lapply(cyto, function(x){
genes = x$Genes
vals = x$Median_roGFP2_ratio
qt.top = quantile(vals, 0.75)
selgenes = unique(genes[vals > qt.top])
selgenes = selgenes[selgenes %in% rdx]
})
mitoOX = lapply(mito, function(x){
genes = x$Genes
vals = x$Median_roGFP2_ratio
qt.top = quantile(vals, 0.75)
selgenes = unique(genes[vals > qt.top])
selgenes = selgenes[selgenes %in% rdx]
})
cytoRX = lapply(cyto, function(x){
genes = x$Genes
vals = x$Median_roGFP2_ratio
qt.low = quantile(vals, 0.25)
selgenes = unique(genes[vals < qt.low])
selgenes = selgenes[selgenes %in% rdx]
})
mitoRX = lapply(mito, function(x){
genes = x$Genes
vals = x$Median_roGFP2_ratio
qt.low = quantile(vals, 0.25)
selgenes = unique(genes[vals < qt.low])
selgenes = selgenes[selgenes %in% rdx]
})
topRdxHits = list(
Cytoplasm_Glucose_high_roGFP2 = cytoOX$Glucose,
Cytoplasm_Galactose_high_roGFP2 = cytoOX$Galactose,
Cytoplasm_Glycerol_high_roGFP2 = cytoOX$Glycerol,
Mitochondria_Glucose_high_roGFP2 = mitoOX$Glucose,
Mitochondria_Galactose_high_roGFP2 = mitoOX$Galactose,
Mitochondria_Glycerol_high_roGFP2 = mitoOX$Glycerol,
Cytoplasm_Glucose_low_roGFP2 = cytoRX$Glucose,
Cytoplasm_Galactose_low_roGFP2 = cytoRX$Galactose,
Cytoplasm_Glycerol_low_roGFP2 = cytoRX$Glycerol,
Mitochondria_Glucose_low_roGFP2 = mitoRX$Glucose,
Mitochondria_Galactose_low_roGFP2 = mitoRX$Galactose,
Mitochondria_Glycerol_low_roGFP2 = mitoRX$Glycerol)
rm(cyto, mito, cytoOX, mitoOX, cytoRX, mitoRX)
paste0("% of positive control redox genes (n=51) found in the top/low (25%) roGFp2 ratio = ",
round(sum(rdx %in% unique(unlist(topRdxHits)))/length(rdx) * 100, 2))[1] "% of positive control redox genes (n=51) found in the top/low (25%) roGFp2 ratio = 88.24"Cytoplasm highest (top 25%) normalized roGFP2 ratios
$Cytoplasm_Glucose_high_roGFP2
[1] "TRX2"
$Cytoplasm_Glycerol_high_roGFP2
[1] "MAE1" "MSN4" "ZWF1" "GRX6" "GRX4" "TAL1"
$Cytoplasm_Galactose_high_roGFP2
[1] "AHP1" "YAP1" "SOL3" "TRR2"
$`Cytoplasm_Glucose_high_roGFP2:Cytoplasm_Glycerol_high_roGFP2`
[1] "GSH2"
$`Cytoplasm_Glucose_high_roGFP2:Cytoplasm_Galactose_high_roGFP2`
[1] "CTT1" "SOD1" "GRX2"
$`Cytoplasm_Galactose_high_roGFP2:Cytoplasm_Glycerol_high_roGFP2`
[1] "GLR1" "SOD2"
$`Cytoplasm_Glucose_high_roGFP2:Cytoplasm_Galactose_high_roGFP2:Cytoplasm_Glycerol_high_roGFP2`
[1] "TSA1" "GND1" "RPE1" "GRX1"Cytoplasm lowest (low 25%) normalized roGFP2 ratios
$Cytoplasm_Glucose_low_roGFP2
[1] "SOL3" "GLR1" "ZWF1" "IDP1" "TAL1"
$Cytoplasm_Glycerol_low_roGFP2
[1] "GRX3" "YEF1" "IDP2" "CCP1" "SOD1" "UTR1" "DOT5" "IDP3"
$Cytoplasm_Galactose_low_roGFP2
[1] "SKN7" "GRX5"
$`Cytoplasm_Glucose_low_roGFP2:Cytoplasm_Glycerol_low_roGFP2`
[1] "ALD4"
$`Cytoplasm_Glucose_low_roGFP2:Cytoplasm_Galactose_low_roGFP2`
[1] "GPX1" "MSN4"
$`Cytoplasm_Galactose_low_roGFP2:Cytoplasm_Glycerol_low_roGFP2`
[1] "GRX7"Mitochondria highest (top 25%) normalized roGFP2 ratios
$`Mitochondria_Glucose_high_roGFP2:Mitochondria_Galactose_high_roGFP2:Mitochondria_Glycerol_high_roGFP2`
[1] "RPE1" "SOD2" "IDP3"
$Mitochondria_Glucose_high_roGFP2
[1] "GSH2" "GRX3" "ALD5" "GLR1"
$Mitochondria_Glycerol_high_roGFP2
[1] "AHP1" "TRX1" "SKN7" "IDP2" "MAE1" "GRX5" "PRX1" "MSN2" "GRX6" "GRX4"
[11] "TAL1"
$Mitochondria_Galactose_high_roGFP2
[1] "ZWF1" "GRX1" "GSH1"
$`Mitochondria_Glucose_high_roGFP2:Mitochondria_Glycerol_high_roGFP2`
[1] "TSA1" "IDP1"
$`Mitochondria_Glucose_high_roGFP2:Mitochondria_Galactose_high_roGFP2`
[1] "CTT1" "SOD1"Mitochondria lowest (top 25%) normalized roGFP2 ratios
$Mitochondria_Glucose_low_roGFP2
[1] "YAP1" "PRX1" "ZWF1"
$Mitochondria_Glycerol_low_roGFP2
[1] "GPX2" "CCP1" "GRX1"
$Mitochondria_Galactose_low_roGFP2
[1] "ALD6" "GRX5" "TRX3"
$`Mitochondria_Glucose_low_roGFP2:Mitochondria_Glycerol_low_roGFP2`
[1] "ALD4" "POS5" "TKL1"
$`Mitochondria_Glucose_low_roGFP2:Mitochondria_Galactose_low_roGFP2`
[1] "SKN7" "IDP2" "GND2" "GRX4"
$`Mitochondria_Glucose_low_roGFP2:Mitochondria_Galactose_low_roGFP2:Mitochondria_Glycerol_low_roGFP2`
[1] "YEF1" "GND1" "SOL3" "GRX7"Session information
R version 3.6.2 (2019-12-12)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] DT_0.12 VennDiagram_1.6.20 futile.logger_1.4.3
[4] venn_1.10 UpSetR_1.4.0 gplots_3.0.1.2
[7] ggpubr_0.2.4 magrittr_1.5 ggrepel_0.8.1
[10] RColorBrewer_1.1-2 plotly_4.9.2 ggplot2_3.2.1
[13] reshape2_1.4.3 dplyr_0.8.4 rmdformats_0.3.6
[16] knitr_1.28
loaded via a namespace (and not attached):
[1] Rcpp_1.0.3 tidyr_1.0.2 gtools_3.8.1
[4] assertthat_0.2.1 digest_0.6.23 mime_0.9
[7] R6_2.4.1 plyr_1.8.5 futile.options_1.0.1
[10] evaluate_0.14 httr_1.4.1 pillar_1.4.3
[13] rlang_0.4.4 lazyeval_0.2.2 data.table_1.12.8
[16] gdata_2.18.0 rmarkdown_2.1 labeling_0.3
[19] stringr_1.4.0 htmlwidgets_1.5.1 munsell_0.5.0
[22] shiny_1.4.0 compiler_3.6.2 httpuv_1.5.5
[25] xfun_0.12 pkgconfig_2.0.3 htmltools_0.4.0
[28] tidyselect_1.0.0 tibble_2.1.3 gridExtra_2.3
[31] bookdown_0.17 viridisLite_0.3.0 crayon_1.3.4
[34] withr_2.1.2 later_1.0.0 bitops_1.0-6
[37] jsonlite_1.6.1 xtable_1.8-4 gtable_0.3.0
[40] lifecycle_0.1.0 formatR_1.7 scales_1.1.0
[43] KernSmooth_2.23-16 stringi_1.4.5 farver_2.0.3
[46] ggsignif_0.6.0 promises_1.1.0 ellipsis_0.3.0
[49] admisc_0.12 vctrs_0.2.2 lambda.r_1.2.4
[52] tools_3.6.2 Cairo_1.5-11 glue_1.3.1
[55] purrr_0.3.3 crosstalk_1.0.0 fastmap_1.0.1
[58] yaml_2.2.1 colorspace_1.4-1 caTools_1.18.0